1. Timo Elolähde 1 Traffic Model System and Emission Calculations of the Helsinki Metropolitan Area Council

2. Timo Elolähde2 General information about the area

3. Timo Elolähde3 Location of Helsinki in Europe

4. Timo Elolähde4 Definitions of areal divisions YTV area includes the cities of Helsinki, Espoo, Vantaa and Kauniainen. Surrounding areas include eight municipalities around the YTV area. Helsinki region = YTV area + surrounding area = 12 municipalities Metropolitan area is used to describe an area contained within approximately a 100 kilometre radius from Helsinki. It consists of 72 municipalities.

5. Timo Elolähde5 Tampere Mikkeli Lahti Hämeenlinna Tammisaari Kotka Hämeenlinna Lahti Kotka YTV Tammisaari PKS 47,000 commuters in 1980 88,000 commuters in 1990 108,000 commuters in 2002 YTV Turku YTV Proportion of commuters in the municipality’s work force Over 35 % 10 - 35 % 2 - 10 % Commuting in the Helsinki Metropolitan Area 1980–2002

6. 6 Timo Elolähde Population Jobs 31.12.2004 31.12.2003 in YTV area Helsinki Espoo Kauniainen Vantaa 559 000 369 000 227 400 104 000 8 500 2 700 185 400 95 000 980 300 0 200000 400000 600000 800000 570 700 Population and the number of jobs in the YTV Area

7. Timo Elolähde7 YTV area target network in 2030

8. Timo Elolähde8 Journeys made daily by public transport and by car within the YTV area Journeys (1000/day) 66 53 42 39 0 25 50 75 100 1966 1976 1988 1995 2000 2005 Share taken by public transport (%) (38) 0 500 1 000 1 500 19661976 19882000 2005 Private car Public transport 1995

9. Timo Elolähde9 Traffic model system

10. Timo Elolähde10 Traffic model system Traffic is divided into three parts internal trips made by the inhabitants of the region trips generated by Helsinki- Vantaa airport (air passengers and employees) external trips (cars only) freight transport (vans and lorries) Modes walk, bicycle public transit car (as driver or passenger) Trip categories home-based work trips home-based school trips other home-based trips non-home-based trips Time periods morning peak hour average hour of the day evening peak hour

11. Timo Elolähde11 Traffic model system Tools Emme/2 macros (contain Unix file handling commands) SAS programs (preparation of input, writing some macros) FORTRAN programs (summary of results) Unix scripts (renaming output files)

12. Timo Elolähde12 Feedback in the four-step model system

13. Timo Elolähde13 Model types trip categorytrip generationmode choicedestination choice home-based work trips trips / person working logit model home-based school trips trips / person of school age distance table (distribution) logit model other home- based trips trips / inhabitantlogit model non-home-based trips trips / inhabitantlogit model

14. Timo Elolähde14 Logit model and logsum

15. Timo Elolähde15 Variables used in models Mode choice models nr of transfers, transit travel time, transit or car travel cost, transit or car parking place availability (arriving trips / parking place) parking cost cars/household ln(distance), walk or bicycle distance 0-5 km, walk or bicycle distance 5-10 km, walk or bicycle dummy variables Destination choice models logsum of mode choice scale factor (inhabitants, jobs) ln(jobs) dummy variables

16. Timo Elolähde16 Mode combinations possible Influence of the number of modes (ms149, ms199, ms249, ms299) on text registers and description fields of matrices (e.g. ”morning peak %t2% work trips”) text register34-45 t1Walk+bicycle Walk t2transitBus+tramtransitBus+tram t3Car t4NO BIKE bicycle t5NO RAILHeavy railNO RAILHeavy rail

17. Timo Elolähde17 Principles applied in coding macros The same selection of possible variables in all models (except school trips) No constants in the model formulas but the coefficients of the models are in scalars Systematics in matrix numbers If a variable is not in the model, its coefficient is zero Only the number of the first input matrix is given as a macro parameter, other consecutive numbers are calculated (e.g. nr of transfers in matrix %2%, transit time in matrix r2=%2%+1) Logical scalars (school trip models in macro school_%ms250%.mac, where ms250=96 or ms250=2001)

18. Timo Elolähde18 Scalars containing the coefficients

19. Timo Elolähde19 Writing an Emme/2 macro with a SAS program Why? Do you want to copy and paste this section 24 times and edit the parts which are underlined? Solution: Give the changing part as data cards and write the rest of the macro with a SAS program (or with some programming language). 1 y ms311 y wt24h home-based work trips ~?q=1 y mf301 y gn01,gn04 o + ~?b=1 2

20. Timo Elolähde20 Essential parts of the SAS program filename outfi2 'K:\Emme2\summary_matr_demo2.mac'; data matr; length mxnro msnro $ 5 name $ 6 descr $ 40; input mxnro $ 4-8 msnro $ 10-14 name $ 16-21 descr $ 23-62 ; cards; mo09 ms301 nrinha total nr of inhabitants mf301 ms311 wt24h home-based work trips 24h ms999 last line ; data _null_; set matr; file outfi2; if _N_ = 1 then do; put "~#" / "~#** calculate sums of vectors" / " 3.21" ; end;

21. Timo Elolähde21 Essential parts of the SAS program nro = substr(msnro,3,3); if (nro ne '999') then do; put "~# *** matrix " _N_ " *** " ; put " 1" / " y" / msnro $ 2-6 / " y" / name $ 2-7 / descr $ 2-41 / "~?q=1" / " y" // mxnro $ 2-6 /// " y" ; if (substr(mxnro,1,2) = 'mo') then put " gn01,gn04" // " +" ; else if nro in ('311') then put " gn01,gn04" // " o" // " +" / " +" ; put "~?b=1" / " 2" ; end; if nro = '999' then do; put " q" / "~#** output the list of scalars" / " reports=summary_matr_demo.txt" / " 3.14" / " 2" / " ms" / "~?b=1" / " 2" / " q" / " reports=%1%" / "~/ *** summary_matr_demo.mac ***" ; end; run;

22. Timo Elolähde22 Estimation of models

23. Timo Elolähde23 Traffic surveys Internal trips trips made by the inhabitants of the YTV area (four cities) during one day (24 h) in autumn 2000 personal trip diary interview, 8,666 persons and 28,553 trips Trips generated by Helsinki-Vantaa airport 875 air passengers and 801 employees (flying and non-flying) survey made in autumn 2001 External trips and freight transport origin-destination study made in autumn 1988

24. Timo Elolähde24 Model estimation Internal trips estimation made by Ms Nina Karasmaa (Helsinki University of Technology, Transportation Engineering) Alogit program More than 50 model sets were estimated and tested Differences e.g. in number of modes and model hierarchy (mode choice after destination choice or vice versa) Three modes in the model set selected. Trips generated by Helsinki-Vantaa airport estimation made by Mr Jyrki Rinta-Piirto (Strafica Ltd) External trips and freight transport models estimated in 1990 are based in changes in land use.

25. Timo Elolähde25 Emission calculations

26. Timo Elolähde26 ”Minor” problem in emission calculations Traffic models produce demand matrices for three weekday hours. Finnish Meteorological Institute needs emissions for every hour of the year for dispersion calculations.

27. Timo Elolähde27 Principle of emission calculations

28. Timo Elolähde28 Emission calculations Tools Emme/2 macros (contain Unix file handling commands) FORTRAN programs (copying or interpolation from link data of 10+7+7 hours to 14+17+17 hours and summary of results) Unix scripts (dialog of FORTRAN run, renaming output files) Emission factors fuel consumption, CO2, SO2, NOx, particles (PM), CO, HC polynomial functions of average speed (from assignment)

29. Timo Elolähde29 Examples of emission factors: NOx emissions of cars and vans

30. Timo Elolähde30 Examples of emission factors: NOx emissions of trucks and buses

31. Timo Elolähde31 Examples of emission factors: CO2 emissions of cars and vans

32. Timo Elolähde32 Examples of emission factors: CO2 emissions of trucks and buses

33. Timo Elolähde33 Proportions of vehicle types in emission calculations ( volau )

34. Timo Elolähde34 Proportions of vehicle types in emission calculations ( volad and bus)

35. Timo Elolähde35 Regression models in emission calculations The regression models have been estimated using volume counts on four cordon lines. For auto assignment, the volumes (car+van and truck) for each hour of the day (10+7+7) are used as regressands and three forecasted hours (morning peak, evening peak and an average hour of the day) as regressors of the model. The models are used for calculating the demand matrices for each hour. For transit assignment, the bus volumes for each hour of the day (3*24) are used as regressands and two forecasted hours (morning peak and an average hour of the day) as regressors of the model. The models are used for calculating the link volumes and emissions for each hour.

36. Timo Elolähde36 Emission calculations emission on regular link [kg/h] = volume [veh/h] * length [km] * emission [g/km/veh] / 1000 cold starts (three classes of motor temperature) and emissions of connector links handled as emissions of the area (in the centroid) example of copying and interpolation of the emission (from 10+7+7 hours to 14+17+17 hours)

37. Timo Elolähde37 Principle of emission calculations (repeated)

38. Timo Elolähde38 Thank you for your patience and interest! Any questions?